ML Model Deployment
ML Model Deployment is the process of making a trained machine learning model available in a production environment so it can generate predictions or decisions using real-world data.
What is ML Model Deployment?
ML model deployment involves moving a validated model from development into an environment where applications or users can access it. This can include integrating the model with APIs, applications, or data pipelines and configuring the infrastructure needed to serve predictions reliably.
Why is ML Model Deployment Important?
Deployment turns a trained model into a usable production system. Effective deployment helps organizations deliver predictions consistently, manage model versions, monitor performance, and identify issues such as data drift or declining model accuracy after release.
Common use cases
ML model deployment is commonly used for fraud detection, recommendation systems, predictive maintenance, customer analytics, computer vision, natural language processing, and other production AI applications.